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Due to the expanding scope of machine learning (ML) to the fields of sensornetworking, cooperative robotics and many other multi-agent systems,distributed deployment of inference algorithms has received a lot of attention.These algorithms involve collaboratively learning unknown parameters fromdispersed data collected by multiple agents. There are two competing aspects insuch algorithms, namely, intra-agent computation and inter-agent communication.Traditionally, algorithms are designed to perform both synchronously. However,certain circumstances need frugal use of communication channels as they areeither unreliable, time-consuming, or resource-expensive. In this paper, wepropose gossip-based asynchronous communication to leverage fast computationsand reduce communication overhead simultaneously. We analyze the effects ofmultiple (local) intra-agent computations by the active agents betweensuccessive inter-agent communications. For local computations, Bayesiansampling via unadjusted Langevin algorithm (ULA) MCMC is utilized. Thecommunication is assumed to be over a connected graph (e.g., as indecentralized learning), however, the results can be extended to coordinatedcommunication where there is a central server (e.g., federated learning). Wetheoretically quantify the convergence rates in the process. To demonstrate theefficacy of the proposed algorithm, we present simulations on a toy problem aswell as on real world data sets to train ML models to perform classificationtasks. We observe faster initial convergence and improved performance accuracy,especially in the low data range. We achieve on average 78% and over 90%classification accuracy respectively on the Gamma Telescope and mHealth datasets from the UCI ML repository.

Asynchronous Local Computations in Distributed Bayesian Learning